Question

df <- data.frame(category=c("cat1","cat1","cat2","cat1","cat2","cat2","cat1","cat2"),
                 value=c(NA,2,3,4,5,NA,7,8))

我想在上面的数据框中添加一个新列，该列采用value列的累积平均值，而不考虑NA。是否可以使用dplyr执行此操作？我试过了

df <- df %>% group_by(category) %>% mutate(new_col=cummean(value))

但cummean只是不知道如何处理NAs。

编辑：我不想将NAs算为0。

Answer 1

您可以使用ifelse将NA视为0 cummean来电：

library(dplyr)

df <- data.frame(category=c("cat1","cat1","cat2","cat1","cat2","cat2","cat1","cat2"),
                 value=c(NA,2,3,4,5,NA,7,8))

df %>%
  group_by(category) %>%
  mutate(new_col = cummean(ifelse(is.na(value), 0, value)))

输出：

# A tibble: 8 x 3
# Groups:   category [2]
  category value new_col
  <fct>    <dbl>   <dbl>
1 cat1       NA     0.  
2 cat1        2.    1.00
3 cat2        3.    3.00
4 cat1        4.    2.00
5 cat2        5.    4.00
6 cat2       NA     2.67
7 cat1        7.    3.25
8 cat2        8.    4.00

编辑：现在我看到这与忽略NAs不同。

试试这个。我按一个列进行分组，该列指定值是否为NA，意味着cummean可以在不遇到任何NA的情况下运行：

library(dplyr)

df <- data.frame(category=c("cat1","cat1","cat2","cat1","cat2","cat2","cat1","cat2"),
                 value=c(NA,2,3,4,5,NA,7,8))

df %>%
  group_by(category, isna = is.na(value)) %>%
  mutate(new_col = ifelse(isna, NA, cummean(value)))

输出：

# A tibble: 8 x 4
# Groups:   category, isna [4]
  category value isna  new_col
  <fct>    <dbl> <lgl>   <dbl>
1 cat1       NA  TRUE    NA   
2 cat1        2. FALSE    2.00
3 cat2        3. FALSE    3.00
4 cat1        4. FALSE    3.00
5 cat2        5. FALSE    4.00
6 cat2       NA  TRUE    NA   
7 cat1        7. FALSE    4.33
8 cat2        8. FALSE    5.33

Answer 2

选项是在计算cummean之前删除值。在此方法中，NA值的行不会计入cummean计算。不确定OP是否希望在计算中将NA值视为0。

df %>% mutate(rn = row_number()) %>%
  filter(!is.na(value)) %>%
  group_by(category) %>%
  mutate(new_col = cummean(value)) %>%
  ungroup() %>% 
  right_join(mutate(df, rn = row_number()), by="rn") %>%
  select(category = category.y, value = value.y, new_col) %>%
  as.data.frame()
#    category value  new_col
# 1     cat1    NA       NA
# 2     cat1     2 2.000000
# 3     cat2     3 3.000000
# 4     cat1     4 3.000000
# 5     cat2     5 4.000000
# 6     cat2    NA       NA
# 7     cat1     7 4.333333
# 8     cat2     8 5.333333

使用cummean with group_by并忽略NA

2 个答案: